I have been analysing a study where around 500 participants each were given a survey, so there were repeated measurements of each participant. Participants were male and female uni students who were presented with 4 character descriptions differing on gender (male/female) and health behavior (healthy/unhealthy), which they had to judge on, for example, weakness. Analyses I have conducted are paired-sample t-tests and two-way repeated measures ANOVAs. Now my supervisors want me to add control variables into the analysis, namely; participant gender, participant SES, participant sexual orientation, etc. by using an ANCOVA. However, I keep finding that all these individual differences in the participants are already controlled for by design, as repeated-measures ANOVA is an analysis within subjects thus individual differences don't play a role. This would mean that significance I have found now is valid and not confounded by the individual characteristics of the participants. Is this correct?
However, then I am confused on how to see whether the effect of the vignette on the dependent variable (e.g., weakness) differs according to participant gender and so on. Do I have to use a mixed ANOVA for this?
For clarification: Each participant read 4 vignettes with character descriptions, these descriptions depicted 2 x 2 manipulated actor variables: health behavior (healthy or unhealthy) x gender (male or female). So these were the independent variables, two categorical within-subject factors. Dependent variables were measured per vignette, for example, the weakness dependent variable was measured by averaging the scores on 4 Likert items (e.g., "To what extent does this person come across as insecure?"). Also, participant characteristics were measured (age, gender, health behavior, education, etc.). Chunck of data:
PpNr = Participant number, Weak_C1X = Average weakness judgment of healthy male, Weak_C2X = Average weakness judgment of unhealthy male, Weak_C3X = Average weakness judgment of healthy female, Weak_C4X = Average weakness judgment of unhealthy female, SES = Socio-economic status of participant, Age = Age of participant, Gender = Gender of participant